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This paper presents the prediction of aging state of oil impregnated insulating Kraft paper using textural properties. Under conditions of prolonged thermal stress, the insulating capacity of Kraft paper decreases due to carbonization and degradation of cellulose. To analyze these effects, a new accelerated aging kit is developed for the experiments. Four groups of samples are collected, and the morphology is examined. Texture feature is extracted for the samples using normalization and Gray-Level Co-occurrence Matrix (GLCM). Supervised (Support Vector Machine (SVM)) and unsupervised (K-means) machine intelligence methods are trained to classify images based on the feature. In addition, new samples are checked for feasibility of the prediction of aging state. The calculated training accuracy of SVM is 92.67%, and for k-means is 95.0%, on the other hand, the testing accuracy of SVM is 91.0%, and for k-means is 92.6%. Therefore, this machine intelligence-based methodology using image features is effective in classifying the aging state of the Kraft Insulating Paper. [ABSTRACT FROM AUTHOR]

Timely identification of the aging stage of the transformer can effectively ensure the safe operation of the transformer and prevent the aging fault of the transformer. This work proposes the aging diagnosis of transformer using the Raman spectroscopic recurrence plot combined with learning vector quantization (LVQ) neural network. Oil-paper insulation samples at different aging stages are obtained by accelerated thermal aging tests. Acquisition of Raman spectra of insulating oil is from 800 to 3000 cm−1 and converts to the Raman spectroscopic recurrence plot. The gray-level cooccurrence matrix (GLCM) is used to extract the characteristic information of the Raman spectroscopic recurrence plot. The optimal number of neurons in competitive layer of LVQ neural network is determined by the experimental method. A transformer aging diagnosis model based on the Raman spectroscopic recurrence plot is established. The results show that the diagnostic accuracy of the proposed diagnostic model reached 95%. The Raman spectroscopic recurrence plot has more abundant aging “fingerprint” characteristic information and the proposed diagnostic method shows potential for monitoring oil-paper insulation. [ABSTRACT FROM AUTHOR]

Under long-term synergy effect of multi-factors, especially the thermal stress, insulating paper will be degraded and its insulation performance will decline due to carbonization and degradation of cellulose. This paper presents an optical approach for aging feature extraction of the insulating paper, where one of the image processing methods called texture analysis is utilized. By conducting laboratory accelerated thermal aging tests, insulating paper samples with different aging conditions for both Nomex and Kraft, evaluated with the aging time, are prepared. After taking optical microscopic images of insulating paper samples belong to different aging groups, up to 14 texture features are extracted using the gray-level co-occurrence matrix (GLCM). With different feature selection methods applied, several of them are finally selected to represent the aging condition of insulating. Numerical tests with both supervised and unsupervised algorithms, as well as a linear regression method verifies the validity of these features in characterizing the aging condition of the insulating paper. [ABSTRACT FROM PUBLISHER]

Detecting insulation performance through testing the Raman spectroscopy of transformer oil is noninvasive and shows great promise in on-site diagnosis. The information, stored in spectra, that may reflect aging states of insulation aims to be effectively unfolded and provides decision support as well. In this article, a transformation method based on intrinsic product arithmetic is used to transform the 1-D Raman spectral data into 2-D image data. With the help of CNN based on the dimensionality expanding method, diagnosis of oil–paper insulation with different aging states based on the Raman spectroscopy was realized. The network-based oil–paper insulation aging diagnosis model has an accuracy of 95% in ten aging categories of 60 test samples and 100% in four aging categories. The result shows the trained CNN was able to identify the differences of transformer oil samples at each aging stage through visualized Raman spectral image. This method provides a more accurate determination method for diagnosing oil-immersed transformers’ aging conditions; it also develops an available method for a more detailed evaluation of insulation aging levels. [ABSTRACT FROM AUTHOR]

Paper surfaces under the microscopic view are observed to be formed by intertwisted wood fibers. Such structures of paper surfaces are unique from one location to another and are almost impossible to duplicate. Previous work used microscopic surface normals to characterize such intrinsic structures as a “fingerprint” of paper for security and forensic applications. In this work, we examine several key research questions of feature extraction in both scientific and engineering aspects to facilitate the deployment of paper surface-based authentication when flatbed scanners are used as the acquisition device. We analytically show that, under the unique optical setup of flatbed scanners, the specular reflection does not play a role in norm map estimation. We verify, using a larger dataset than prior work, that the scanner-acquired norm maps, although blurred, are consistent with those measured by confocal microscopes. We confirm that, when choosing an authentication feature, high spatial-frequency subbands of the heightmap are more powerful than the norm map. Finally, we show that it is possible to empirically calculate the physical dimensions of the paper patch needed to achieve a certain authentication performance in equal error rate (EER). We analytically show that log(EER) is decreasing linearly in the edge length of a paper patch. [ABSTRACT FROM AUTHOR]

Recycled paper is extensively used worldwide. In the last decades, its market has expanded considerably. The increasing use of recycled paper in papermaking has led to the production of paper containing several types of impurities. Consequently, wastepaper mills are forced to implement quality control schemes for evaluating the incoming wastepaper stock, thus guarantying the specifications of the final product. The main objective of this work is to present a fast and reliable system for identifying different paper types. Therefore, undesirable paper types can be refused, improving the performance of the paper machine and the final quality of the paper manufactured. For this purpose, two fast techniques, i.e., Fourier transform midinfrared (FTIR) and reflectance near infrared (NIR), were applied to acquire the infrared spectra of the paper samples. Next, four processing multivariate methods, i.e., principal component analysis, canonical variate analysis (CVA), extended CVA (ECVA), and support vector machines (SVMs), were employed in the feature-extraction or dimension-reduction stage. Afterward, the k nearest neighbor (kNN) algorithm was used in the classification phase. Experimental results show the usefulness of the proposed methodology and the potential of both FTIR and NIR spectroscopic methods. Using the FTIR spectrum in association with SVM and kNN, the system achieved a maximum classification accuracy of 100%, whereas using the NIR spectrum in association with ECVA or SVM and kNN, the system achieved a maximum classification accuracy of 96.4%. [ABSTRACT FROM PUBLISHER]

The widespread communication of academic ideas and research achievements through literature and media has generated massive academic big data. Analyzing such academic big data and discovering knowledge can discover comprehensive and predictive academic intelligence and provide corresponding services, which are valuable for scholars, journals, institutions and governments for career assessment, topic selection, funding management and resource allocation. This paper proposes a heterogeneous knowledge-learning method for understanding and predicting academic impact in the transportation field. The proposed method is illustrated on the academic big data collected from the papers published in 34 transportation journals from 2008 to 2018. We extract four types of features including bibliometric, altmetric, network and semantic features, and build hybrid feature embedding via TransR and Doc2vec that involving domain knowledge. Further, an academic impact prediction model for articles named as Hy-LSTM-Att is proposed, which weighs the hybrid features by the attention mechanism and predicts academic impact with the bi-LSTM recurrent neural networks. Experimental results demonstrate that the proposed Hy-LSTM-Att model outperforms competing shallow and deep learning models. Additionally, the feature ablation experiments illustrate that the four types of features positively contribute to the performance of impact prediction. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a very challenging task of automatically generating presentation slides for academic papers. The generated presentation slides can be used as drafts to help the presenters prepare their formal slides in a quicker way. A novel system called PPSGen is proposed to address this task. It first employs the regression method to learn the importance scores of the sentences in an academic paper, and then exploits the integer linear programming (ILP) method to generate well-structured slides by selecting and aligning key phrases and sentences. Evaluation results on a test set of 200 pairs of papers and slides collected on the web demonstrate that our proposed PPSGen system can generate slides with better quality. A user study is also illustrated to show that PPSGen has a few evident advantages over baseline methods. [ABSTRACT FROM PUBLISHER]

This paper addresses digital staining and classification of the unstained white blood cell images obtained with a differential contrast microscope. We have data coming from multiple domains that are partially labeled and partially matching across the domains. Using unstained images removes time-consuming staining procedures and could facilitate and automatize comprehensive diagnostics. To this aim, we propose a method that translates unstained images to realistically looking stained images preserving the inter-cellular structures, crucial for the medical experts to perform classification. We achieve better structure preservation by adding auxiliary tasks of segmentation and direct reconstruction. Segmentation enforces that the network learns to generate correct nucleus and cytoplasm shape, while direct reconstruction enforces reliable translation between the matching images across domains. Besides, we build a robust domain agnostic latent space by injecting the target domain label directly to the generator, i.e., bypassing the encoder. It allows the encoder to extract features independently of the target domain and enables an automated domain invariant classification of the white blood cells. We validated our method on a large dataset composed of leukocytes of 24 patients, achieving state-of-the-art performance on both digital staining and classification tasks. [ABSTRACT FROM AUTHOR]

Recognizing actions from still images has been popularly studied recently. In this paper, we model an action class as a flexible number of spatial configurations of body parts by proposing a new spatial sum–product network (SPN). First, we discover a set of parts in image collections via unsupervised learning. Then, our new spatial SPN is applied to model the spatial relationship and also the high-order correlations of parts. To learn robust networks, we further develop a hierarchical spatial SPN method, which models pairwise spatial relationship between parts inside subimages and models the correlation of subimages via extra layers of SPN. Our method is shown to be effective on two benchmark data sets. [ABSTRACT FROM PUBLISHER]

This paper addresses classifying different common partial discharge (PD) types under different acoustic emission (AE) measurement conditions. Four types of PDs are considered for the multi-class classification problem, namely; PD from a sharp point to ground plane, surface discharge, PD from a void in the insulation, and PD from semi parallel planes. The collected AE signals are processed using pattern classification techniques to identify their corresponding PD types. The measurement conditions include the influences of various PD locations, oil temperatures, and having a barrier in the line-of-sight between the PD source and the AE sensor. A recognition rate of 94% is achieved when classifying the different PD types measured at the same conditions. In addition, it has been found that the different PD source locations, oil temperatures, and barrier insertion have an impact on the recognition rate. However, by including AE samples at these different conditions in the training process, a recognition rate around 90% for all cases is achieved. [ABSTRACT FROM AUTHOR]

The prototype O system seamlessly integrates Web searching and browsing on touch-enabled tablets using two-handed gesture interaction, much like handling and marking content on a piece of paper. The system also leverages contextual information to infer user intent and significantly improve search results over traditional keyword-based approaches, enabling users to go beyond websites' "walled garden"' and more fully explore information objects. [ABSTRACT FROM PUBLISHER]

The semiconductor industry is the core industry of the information age. As a key link in the semiconductor industry, wafer fabrication plays a key role in its development. In the testing stage of the wafer, each die of the wafer is detected and marked, and a wafer map with a certain spatial pattern can be formed. The analysis and classification of these spatial patterns can identify the cause of wafer defects, thereby improving production yield. However, as wafer size increases, line widths become smaller, etc., the probability of a mixed defect mode wafer pattern increases. Moreover, the mixed defect mode wafer map is more difficult to identify and classify than the single defect mode wafer map. Therefore, this paper proposes an improved deep convolutional neural network (DCNN) structure model for the recognition and classification of mixed defect pattern wafer maps. From the perspective of increasing the width of the DCNN, the improved network structure can avoid problems such as over-fitting and limited extraction of features due to the continuous deepening of the DCNN. The network is called Multi-Path DCNN (MP-DCNN) structure. The experimental results show that the proposed Multi-Path DCNN structure has better performance and higher classification accuracy than existing methods. [ABSTRACT FROM AUTHOR]

The article reports on research into sketch-understanding systems for tablet personal computers (tablet PC). It discusses the work of the Sketch Understanding Group, of which the author is a member, at the Massachusetts Institute of Technology (MIT) on developing a "magic paper," or "intelligent paper," a paper-like surface that can convert sketches of physical systems into animated computer graphics. Sketch-understanding systems go beyond traditional computer-aided design (CAD) programs that produce diagrams, and beyond handwriting recognition systems.

In recent years, multilayer networks have served as effective models for addressing and analyzing real-world systems with multiple relationships. Among these scenarios, the community detection (CD) problem is one of the most prominent research hotspots. Although some research on multilayer network CD (MCD) has been proposed to address this problem, most studies focus only on topological structures. Therefore, their algorithms cannot extract the most out of complementary network information, such as node similarities and low-rank features, which may lead to unsatisfactory accuracy. To tackle this problem, this paper proposes a novel multi-objective evolutionary algorithm based on consensus prior information (MOEA-CPI). The proposed algorithm takes full advantage of prior information to guide the MOEA with respect to topological structures, initializations, and the optimization process. More specifically, this paper first extracts two kinds of prior information, i.e., graph-level and node-level information, based on Node2vec and Jaccard similarity, respectively. Then, the prior layer and a high-quality initial population are constructed on the basis of the graph-level information. During the optimization process, the genetic operator, which integrates the weighting strategy and node-level information, is applied to guide the algorithm to distribute similar nodes into the same community. Extensive experiments are implemented to prove the superior performance of MOEA-CPI over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for Virtual Metrology (VM) in plasma etch processes based on analysis of all time and wavelength samples of Optical Emission Spectroscopy (OES) signals. The new method flattens each OES signal into a single vector, after which Singular Value Decomposition (SVD) is performed on the matrix formed by vectors of flattened OES signals in the training dataset. Low rank SVD projections of flattened and standardized OES recordings served as inputs for Ridge Regression, Artificial Neural Network, and Random Forest based VM models. A VM study is then conducted on a dataset gathered from a major 300 mm wafer fabrication facility, showing that the use of newly proposed SVD-based OES features consistently outperformed benchmark VM model features. Additional analysis of feature importance performed based on the analytically tractable Ridge Regression VM model form demonstrated distinct time-frequency patterns of OES signal portions that were highly informative for prediction of relevant Critical Dimensions, clearly justifying the need to use the entire OES signals for VM. [ABSTRACT FROM AUTHOR]

Currently, SLAM (simultaneous localization and mapping) systems based on monocular cameras cannot directly obtain depth information, and most of them have problems with scale uncertainty and need to be initialized. In some application scenarios that require navigation and obstacle avoidance, the inability to achieve dense mapping is also a defect of monocular SLAM. In response to the above problems, this paper proposes a method which learns depth estimation by DenseNet and CNN for a monocular SLAM system. We use an encoder-decoder architecture based on transfer learning and convolutional neural networks to estimate the depth information of monocular RGB images. At the same time, through the front-end ORB feature extraction and the back-end direct RGB-D Bundle Adjustment optimization method, it is possible to obtain accurate camera poses and achieve dense indoor mapping when using estimated depth information. The experimental results show that the monocular depth estimation model used in this paper can achieve good results, and it is also competitive in comparison with the current popular methods. On this basis, the error of camera pose estimation is also smaller than traditional monocular SLAM solutions and can complete the dense indoor reconstruction task. It is a complete SLAM system based on monocular camera. [ABSTRACT FROM AUTHOR]

Distracted or drowsy driving is unsafe driving behavior responsible for thousands of crashes every year. Studying driver behavior has challenges associated with observing drivers in their natural environment. The naturalistic driving study (NDS) has become the most sought-after approach, since it eliminates the bias of a controlled setup, allowing researchers to understand drivers’ behavior in real-world scenarios. Video recordings collected in NDS research are incredibly insightful in identifying driver errors. Computer vision techniques have been used to autonomously analyze video data and classify drivers’ behavior. While computer vision scientists focus on image analytics, NDS researchers are interested in the factors impacting driver behavior. This survey paper makes a concerted effort to serve both communities by comprehensively reviewing studies, describing their data collection, computer vision techniques implemented, and performance in classifying driver behavior. The scope is limited to studies employing at least one camera observing the driver inside a vehicle. Based on their objective, papers have been classified as detecting low-level (e.g. head orientation) or high-level (e.g. distraction detection) driver information. Papers have been further classified based on the datasets they employ. In addition to twelve public datasets, many private datasets have also been identified, and their data collection design is discussed to highlight any impact on model performance. Across each task, algorithms employed and their performance are discussed to establish a baseline. A comparison of different frameworks for NDS video data analytics throws light on the existing gaps in the state-of-the-art that can be addressed by future computer vision research. [ABSTRACT FROM AUTHOR]

Surface defect is a serious problem in real-world manufacturing system and it is important to use vision-based recognition to ensure the surface quality of products. Currently, due to the ability of automatic feature extraction, deep learning models, such as convolutional neural network (CNN), have been widely used in this area. However, these CNN-based models may not solve a problem well - inter-class similarities and intra-class variations (ISIV), which affect their ability of feature extraction and thus influence their recognition performance. To address this problem, this paper introduces a graph guidance mechanism into CNN to improve the ability of feature extraction, called Graph guided Convolutional Neural Network (GCNN). Firstly, GCNN defines a graph by computing the similarities between training samples. Secondly, the graph is introduced into VGG11, a popular CNN structure, to increase the inter-class distances and decrease the intra-class distances between defect samples. Meanwhile, a learnable coefficient is introduced into the training process to balance the effect of graph guidance automatically. The experimental results on four famous surface defect datasets demonstrate that the graph guidance helps CNN models have better ability of feature extraction and thus achieve better performance. Compared with state-of-the-art models, the proposed method can achieve the best performance. Furthermore, the final discussion shows that the learnable coefficient can help the proposed model to gain better performance, and that the proposed model increases a little computation cost compared to its original CNN model. Note to Practitioners—This paper is motivated by the problem in real-world manufacturing process – inter-class similarities and intra-class variations. Most of current CNN-based models may not solve this problem well, which limits their applications in real-world system. This paper proposes a graph guidance mechanism and introduces it to the training of CNN. The proposal can improve the ability of feature extraction, and thus have better performance than those without the graph guidance. By applying the proposal to four examples, the results show that the proposal is more feasible and effective than the state-of-the-art models in surface defect recognition. Furthermore, the mechanism assists the training of CNN and is removed in the process of recognition, so it will not increase time and occupied memory in recognition, and meanwhile, only limited computation cost is increased in training process. [ABSTRACT FROM AUTHOR]

The vision recognition module is one of the very important modules in the normal operation of the intelligent plant cultivation robot. In the original vision module, by improving the original YOLOV3(The algorithm proposed in YOLOv3: An Incremental Improvement), a more excellent YOLOV3 was obtained to recognize plants under normal conditions. However, the improved YOLOV3 does not perform well in the visual recognition process when there is a lot of noise interference. To make the visual recognition module continue to work properly, in this paper, the better Cycle GAN (generative adversarial networks) model is proposed to deal with noise based on three common noises, and finally, through a lot of experiments, it is proved that the improvement has some significance for the vision recognition module, which extends the use of the vision recognition module and makes the intelligent plant cultivation robot avoid the interference of noise. [ABSTRACT FROM AUTHOR]

Emotion recognition using brain wave signals involves using high dimensional electroencephalogram (EEG) data. In this paper, a window selection method based on mutual information is introduced to select an appropriate signal window to reduce the length of the signals. The motivation of the windowing method comes from EEG emotion recognition being computationally costly and the data having low signal-to-noise ratio. The aim of the windowing method is to find a reduced signal where the emotions are strongest. In this paper, it is suggested, that using only the signal section which best describes emotions improves the classification of emotions. This is achieved by iteratively comparing different-length EEG signals at different time locations using the mutual information between the reduced signal and emotion labels as criterion. The reduced signal with the highest mutual information is used for extracting the features for emotion classification. In addition, a viable framework for emotion recognition is introduced. Experimental results on publicly available datasets, DEAP and MAHNOB-HCI, show significant improvement in emotion recognition accuracy. [ABSTRACT FROM AUTHOR]

Visual surveillance for autonomous power facility inspection is considered to bethe prominent field of study in the power industry. This research field completely focuses on either object detection or image fusion which lacks the overall consideration. By considering this, a single end-to-end object detection method by incorporating the image fusion named Fast Detection Fusion Network (FDFNet) is proposed in this paper to output qualitative fused images with detection results. The parameters in the FDFnet are greatly reduced by sharing the feature extraction network between image fusion and object detection tasks, due to which a huge reduction in computational complexity is achieved. On this basis, the object detection algorithm performance on various types of power facility images is compared and analyzed. For experimentation purposes, an IR (infrared) and VIS (visible) image acquisition system has also been designed. In addition, a dataset named CVPower with different sets of images for power facility fusion detection is constructed for this research field. Experimental results demonstrate that the proposed method can achieve the mAP of not less than 70%, process 2 frames per second, and produce high qualitative fused images. [ABSTRACT FROM AUTHOR]

Machining conditions of real-time identification tools is a key and trending issue for the industry. This paper focuses on identifying whether machining is performed as well as the chatter conditions generated during re-machining processes. Identifying whether or not machining conditions are met allows users to ensure the normal operation of machining equipment and identify situations that do not match the current conditional, so that they can take early action and further save on operational costs for machining. The objective of this paper is to identify the milling machining conditions, and the identified conditions will be categorized into whether cutting is required as well as whether chatter is observed. In order to identify these three conditions, sound and vibration signals are captured by sensors inside the milling machine, and the process of identification is subsequently analyzed and conditions established. In this paper, in order to produce a valid model, the extracted machining signal is characterized as a training model by the properties of Approximate Entropy and Short-Time Fourier Transform, and the k-fold cross-validation criteria is utilized to present the identification results. Finally, In this study, the model recognition rate of support vector machine with approximate entropy was 91.4%. The recognition rate of the convolutional neural network with short time span Fourier transform was 95.5%. Finally, the reduced network architecture can significantly reduce the training time and maintain the recognition rate at 93.6%. [ABSTRACT FROM AUTHOR]

Many real-world classification problems are becoming multi-label in nature, i.e., multiple class labels are assigned to an instance simultaneously. Multi-label classification is a challenging problem due to the involvement of three forms of interactions, i.e., feature-to-feature, feature-to-label, and label-to-label interactions. What further complicates the problem is that not all features are useful, and some can deteriorate the classification performance. Sparsity-based methods have been widely used to address multi-label feature selection due to their efficiency and effectiveness. However, most (if not all) existing methods do not consider the three forms of interactions simultaneously, which could hinder their ability to achieve good performance. Moreover, most existing methods are gradient-based, which are prone to getting stuck at local optima. This paper proposes a new sparsity-based feature selection approach that can simultaneously consider all three forms of interactions. Furthermore, this paper develops a novel sparse learning method based on particle swarm optimisation that can avoid local optima. The proposed method is compared against the state-of-the-art multi-label feature selection methods in terms of multi-label classification performance. The results show that our method performed significantly better in selecting high-quality feature subsets with respect to various feature subset sizes. [ABSTRACT FROM AUTHOR]

Speaker recognition systems (SRSs) are commonly used for biometric identification. However, these systems are vulnerable to adversarial attacks. Several defenses have been proposed but they require high costs in terms of additional data and computational resources to ensure robustness. To address these issues, this paper proposes a low-cost input reconstruction defense method called adaptive F-ratio-based partial masking (AFPM), which utilizes a robust feature extraction process to guarantee high defensibility. The underlying distribution of non-robust features is explored and filtered out by partial masking (PM), which helps maintain a low defense construction cost. An F-ratio-based PM (FPM) defense strategy is proposed by integrating the F-ratio, which reflects the weight of each frequency band for distinguishing between speakers, to balance classification accuracy and defensiveness. AFPM, which introduces an adaptive threshold calculation algorithm to FPM, is proposed to achieve further improved defensiveness and flexibility. Comparative experimental results show that AFPM is low-cost, highly defensive and universal. The construction process of AFPM does not involve training and its implementation does not require the protected SRSs to be retrained, only fine-tuned. While maintaining the classification accuracy at 99.42%, the average defense capability of AFPM against five white-box adaptive attacks is 90.89%, which is 9.23% better than that of the low-cost input reconstruction defense method and 3.77% better than that of the high-cost Parallel WaveGAN (PWG) defense approach. Against grey- and black-box adaptive attacks, FAKEBOB and Kenansville, AFPM reaches maximum defense effects of 96.01% and 74.49%, respectively, surpassing PWG by 4.5% and 65.82%. Furthermore, AFPM is universal and capable of protecting various SRSs against different attack strengths. [ABSTRACT FROM AUTHOR]

Public Bike Systems (PBSs) offer the popular service for the short distance in daily life. The battery powered bike is an interesting and feasible method to extend the bike trip length, which can promote the PBS service but faces the challenges caused by the limited budget for the battery cabinet deployment and user demand. Thus, the realistic problem is how to deploy the cabinets near a part of public bike stations by considering the challenges. This paper is the first to study the novel problem, Cabinet Deployment Problem (CDP) in PBS, based on the features extracted from the real dataset of PBS in Hangzhou China, and proposes our strategies in the case of the Euclidean space and Manhattan model. In the Euclidean space, CDP can be specified as the ${e}$ lectric-bike Set Cover problem (e-SC), and this paper proposes a Greedy Station Coverage algorithm (GSC). Its distributed version, called the Localized Greedy Selection algorithm (LGS), is also presented because of the large amount of bike stations. In many cities, the roads have Manhattan-type directions, i.e., either east-west or south-north. In order to close to the realistic scenario, this paper develops a Genetic Algorithm based Cabinet Search algorithm (GAS) to determine the locations for the cabinet deployment in the Manhattan model. The extensive numerical experiment is conducted for our strategies, which are compared to a straightforward method, the Random Placement Strategy (RPS) under the diverse parameter settings. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of magnification on content-based image search in digital pathology archives and proposes to use multi-magnification image representation. Image search in large archives of digital pathology slides provides researchers and medical professionals with an opportunity to match records of current and past patients and learn from evidently diagnosed and treated cases. When working with microscopes, pathologists switch between different magnification levels while examining tissue specimens to find and evaluate various morphological features. Inspired by the conventional pathology workflow, we have investigated several magnification levels in digital pathology and their combinations to minimize the gap between AI-enabled image search methods and clinical settings. The proposed searching framework does not rely on any regional annotation and potentially applies to millions of unlabelled (raw) whole slide images. This paper suggests two approaches for combining magnification levels and compares their performance. The first approach obtains a single-vector deep feature representation for a digital slide, whereas the second approach works with a multi-vector deep feature representation. We report the search results of 20×, 10×, and 5× magnifications and their combinations on a subset of The Cancer Genome Atlas (TCGA) repository. The experiments verify that cell-level information at the highest magnification is essential for searching for diagnostic purposes. In contrast, low-magnification information may improve this assessment depending on the tumor type. Our multi-magnification approach achieved up to 11% F1-score improvement in searching among the urinary tract and brain tumor subtypes compared to the single-magnification image search. [ABSTRACT FROM AUTHOR]

In recent years, pedestrian and vehicle detection at night has become an important subject of computer vision applications. Because the environment light is weak at night, the common traditional camera-input algorithms often performs poorly. Pedestrians and vehicles in infrared images are usually brighter than the surrounding environment and have salient features. In this paper, a fast saliency map of pedestrian and vehicle targets in infrared images at night is used to achieve rapid acquisition of areas of interest for pedestrians and vehicles. Based on this, we propose a method to refine and separate the target area to obtain accurate pedestrian and vehicle candidate bounding boxes. Specifically, this paper proposed a multi-feature fusion algorithm of pedestrian and vehicle feature extraction combined with support vector machine (SVM) to determine whether the extracted target area really includes pedestrians and vehicles. Our experimental results show that the proposed method can achieve the expected effect of the classification and detection for pedestrians and vehicles at night, and can meet the real-time requirements of actual road scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a differential pricing method and a strategy for determining the time node based on the multi-dimensional characteristics of trains by Markov stochastic process, which focus on the study of dynamic differential pricing of the same class of seats with non-parallel trains with different operating characteristics at the same origin-destination. Firstly, an improved machine learning Boosting model Gradient Boosting Decision Tree(GBDT) is proposed to optimize the price discrimination grouping. Secondly, a dynamic simulation mechanism of passengers’ selection under differential pricing is constructed, which breaks through the traditional measurement method to measure the passengers’ sensitivity to price changes. Thirdly, a new method, which uses Bootstrap to get the dynamic feedback for the adaptability of passengers under a differential pricing system, is proposed to determine the iteration nodes of differential pricing. Finally, based on the Markov property of railway passengers’ selection, the paper breaks through the traditional static pricing mechanism and constructs a dynamic differential pricing solution framework. Taking the Beijing-Shanghai high-speed trains from Beijing South to Shanghai Hongqiao as an example, by comparing with the existing dynamic differential pricing system, it’s proved that the dynamic differential pricing system proposed in this paper can balance the occupancy rate between trains, suppress the fluctuation of occupancy rate, determine the updating node of differential pricing, etc., and hence, is more reasonable and effective than the current system. [ABSTRACT FROM AUTHOR]

Detecting wafer map anomalies is crucial for preventing yield loss in semiconductor fabrication, although intricate patterns and resource-intensive labeled data prerequisites hinder precise deep-learning segmentation. This paper presents an innovative, unsupervised method for detecting pixel-level anomalies in wafer maps. It utilizes an efficient dual attention module with a knowledge distillation network to learn defect distributions without anomalies. Knowledge transfer is achieved by distilling information from a pre-trained teacher into a student network with similar architecture, except an efficient dual attention module is incorporated atop the teacher network’s feature pyramid hierarchies, which enhances feature representation and segmentation across pyramid hierarchies that selectively emphasize relevant and discard irrelevant features by capturing contextual associations in positional and channel dimensions. Furthermore, it enables student networks to acquire an improved knowledge of hierarchical features to identify anomalies across different scales accurately. The dissimilarity in feature pyramids acts as a discriminatory function, predicting the likelihood of an abnormality, resulting in highly accurate pixel-level anomaly detection. Consequently, our proposed method excelled on the WM-811K and MixedWM38 datasets, achieving AUROC, AUPR, AUPRO, and F1-Scores of (99.65%, 99.35%), (97.31%, 92.13%), (90.76%, 84.66%) respectively, alongside an inference speed of 3.204 FPS, showcasing its high precision and efficiency. [ABSTRACT FROM AUTHOR]

Traditional algorithm lacks the ability to generalize across different application scenarios and how to set the stopping conditions of network iteration in Stochastic Configuration Network (SCN) is a problem. This paper proposes an event recognition system based on minimum redundancy, maximum relevance (mRMR) and a modified Stochastic Configuration Network (CWCs-SCN) for the fiber Bragg grating (FBG) sensor system. mRMR is used to analyze the importance of 14th time-frequency domain features in different application scenarios. After that, a modified SCN (CWCs-SCN) neural network is used to solve the stopping condition of the network iteration. The experiments demonstrated that the average recognition accuracy of four kind of human intrusion signal can reach to 96.7%, which is better than others traditional methods. In addition, a more compact neural network is obtained compared to SCN. To verify the generalization ability of the proposed model, it is applied to gear fault diagnosis with an accuracy of 96.3%. This imply that the proposed method in this paper has a wide application prospect. [ABSTRACT FROM AUTHOR]

In this paper, we present a comprehensive review of the imbalance problems in object detection. To analyze the problems in a systematic manner, we introduce a problem-based taxonomy. Following this taxonomy, we discuss each problem in depth and present a unifying yet critical perspective on the solutions in the literature. In addition, we identify major open issues regarding the existing imbalance problems as well as imbalance problems that have not been discussed before. Moreover, in order to keep our review up to date, we provide an accompanying webpage which catalogs papers addressing imbalance problems, according to our problem-based taxonomy. Researchers can track newer studies on this webpage available at: https://github.com/kemaloksuz/ObjectDetectionImbalance. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicle (UAV) inspection has gradually replaced manual inspection of transmission tower, which produces many images. While it is laborious and time-consuming to manually analyze these images, there are also challenges in automatically detecting small-size defective components such as bolts in transmission tower images, due to problems including complex background, small size, and many similar objects of bolts. In this paper, by virtue of multi-scale features and context information, we propose a deep neural network named Camp-Net (Context Information and Multi-Scale Pyramid Network) to identify bolts defect in transmission tower images. First, multi-scale feature fusion combines deep features and shallow features in convolutional networks to detect small-size bolts. Second, context information fusion puts the information around bolts into the detection network to remove the disturbance of complex background and similar objects. An image dataset containing defective bolts and normal bolts is constructed for model training and testing. Experimental results show that bolts with loose pins and bolts without pins among fittings in transmission tower can be accurately identified with the proposed model. The Average Precision (AP) of defective bolts detection of this model can be 11.4% higher than that of the commonly used high performance model, Faster R-CNN. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG). To detect co-saliency (segmentation) accurately, the core problem is to well model inter-image relations between an image group. Some methods design sophisticated modules, such as recurrent neural network (RNN), to address this problem. However, order-sensitive problem is the major drawback of RNN, which heavily affects the stability of proposed CoSOD (CoSEG) model. In this paper, inspired by RNN-based model, we first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU). Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process. Moreover, we design a cross-order contrastive loss (COCL) that can further address order-sensitive problem by pulling close the feature embedding generated from different input orders. We validate our model on five widely used CoSOD datasets (CoCA, CoSOD3k, Cosal2015, iCoseg and MSRC), and three widely used datasets (Internet, iCoseg and PASCAL-VOC) for object co-segmentation, the performance demonstrates the superiority of the proposed approach as compared to the state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

An important aspect of querying whether a product is likely to be forged is to identify its anti-counterfeiting label. However, the use of image processing technology for label-specific texture analysis to quickly and effectively identify the anti-counterfeiting label has been widely studied. Aiming at the defects of the local binary pattern (LBP) and its variants in texture identification, this paper proposes a new texture model for anti-counterfeiting identification, that is, the circular local ternary pattern (CLTP). The highlight of our technology is that it extracts the effective local texture descriptors by using the random features of inkjet printing. This allows for the technology to not only resist the interference of noise and illumination in images of anti-counterfeiting patterns but also to encode and reorganize the fine linear shape structure. Specifically, this paper extracts the CLTP texture feature in the corresponding key areas and forms the final feature histogram vector for comparison through the one-to-one correspondence between the sample image and the inspected image of anti-counterfeiting pattern. Experiments prove that our method not only has high discrimination, stability and effectiveness but also provides a convenient and practical idea for anti-counterfeiting technology. [ABSTRACT FROM AUTHOR]

Since the introduction of adaptive steganography, most of the recent research works seek at designing cost functions that are evaluated against steganalysis methods. While those approaches have been successful, they rely on intuitive principles and ad-hoc costs associated with each pixel or Discrete Cosine Transform (DCT) coefficient. Beyond the empirical assessments, the insights one can get from such approaches are very limited. On the opposite, this paper presents an original method for steganography in JPEG images that exploits a statistical model of the DCT coefficients. Within the framework of hypothesis testing theory, we use a statistical model of covers to derive the analytical expression of the most powerful detector. The objective of the steganographer is to minimize the statistical performance of this “omniscient detector” which represents a “worst-case” scenario for security. This paper shows how this method allows designing effective steganography, in terms of both security and computational complexity, in the two main use cases: when having only one single JPEG image and when the uncompressed image is available, case also known as Side-Informed (SI). A wide range of numerical comparisons shows that the proposed method outperforms the current state-of-the-art especially against the latest and most accurate steganalysis approaches based on Deep Learning. [ABSTRACT FROM AUTHOR]

Network alignment is a critical task in a wide variety of fields. Many existing works leverage on representation learning to accomplish this task without eliminating domain representation bias induced by domain-dependent features, which yield inferior alignment performance. This paper proposes a unified deep architecture (DANA) to obtain a domain-invariant representation for network alignment via an adversarial domain classifier. Specifically, we employ the graph convolutional networks to perform network embedding under the domain adversarial principle, given a small set of observed anchors. Then, the semi-supervised learning framework is optimized by maximizing a posterior probability distribution of observed anchors and the loss of a domain classifier simultaneously. We also develop a few variants of our model, such as, direction-aware network alignment, weight-sharing for directed networks and simplification of parameter space. Experiments on three real-world social network datasets demonstrate that our proposed approaches achieve state-of-the-art alignment results. [ABSTRACT FROM AUTHOR]

Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset. [ABSTRACT FROM AUTHOR]

Benefited from the sampling signal from monitoring sensors installed in electro-mechanical actuator (EMA), data-driven methods, such as deep learning models that can mine adaptive features from monitoring signal, have received wide attention in EMA fault diagnosis. A generalized hypothesis accepted by previous efforts on EMA fault diagnosis, is that target domain datasets (i.e., testing monitoring signal) are drawn from the same distribution with source domain datasets (i.e., training monitoring signal). However, except for the different working loads and velocities, various driving signal waveforms also exits in EMA, which further increase the domain discrepancy challenge and decrease the performance of those deep learning models. Therefore, this paper proposes a deep learning domain adaptation method that introduces joint maximum mean discrepancy (JMMD) into convolutional neural network (CNN), which aims to reduce the domain difference between labeled source domain datasets and unlabeled target domain datasets. In order to simulate domain discrepancy phenomenon, the NASA EMA datasets, sampled from acceleration sensors under variable driving waveforms with different working loads and velocities, are considered in this paper. Experiment results indicate that CNN-JMMD method can reduce the distribution difference and improve the diagnosis accuracy in source and target domains. In comparison with several available methods, extensive experimental evaluations on six domain tasks indicate the effectiveness of the proposed method. In addition, better performance has been obtained by selecting the proper trade-off factor between CNN and JMMD. [ABSTRACT FROM AUTHOR]

Human-machine interface(HMI) technology has gradually become a research hotspot with the continuous development of computer technology and Internet of Things (IOT) technology. Hand gesture recognition (HGR) as an important part of HMI technology has been widely concerned. Among the many technical routes of HGR technology, the wearable HGR technology based on A-mode ultrasound shows great application potential due to its advantages such as lightweight device, free from sensors’ constraint etc. However, in the process of processing A-mode ultrasonic signal, the amplitude of the signal at the main position may vary due to muscle fatigue and strength etc. when the same gesture is repeated some time later. In this paper, we design a method to overcome this problem and accomplish HGR in offline state by setting the threshold and use normalization method for the energy feature of ultrasonic signal according to the threshold. Three machine learning algorithms, including support vector machine (SVM), linear discriminant analysis (LDA) and Naive Bayes (NB) were used to verify the feasibility of this method, and the average recognition accuracy in the experiment reached 95.26%. Signal stability is also verified to be improved. In addition, We design a recognition strategy based on NB algorithm in this paper so that the model can identify the unknown gestures (i.e. gestures that have not been trained by the model). Five known gestures and five unknown gestures were set in the experiment, the average recognition accuracy of the known gestures is 91.2%, and the unknown gestures is 81.8%. This method can be used to optimize the user experience of HGR system. [ABSTRACT FROM AUTHOR]

Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed. The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data distribution of fragments in a dataset. Experiments are conducted on Blip10000 dataset using the anchor fragments provided by TRECVid Video Hyperlinking (LNK) task over the years of 2016 and 2017. This paper shares the empirical insights on a number of issues in cross-modal learning, including the preservation of neighbourhood structure in embedding, model fine-tuning and issue of missing modality, for video hyperlinking. [ABSTRACT FROM AUTHOR]

Multimodal sentiment analysis aims to extract and integrate information from different modalities to accurately identify the sentiment expressed in multimodal data. How to effectively capture the relevant information within a specific modality and how to fully exploit the complementary information among multiple modalities are two major challenges in multimodal sentiment analysis. Traditional approaches fail to obtain the global contextual information of long time-series data when extracting unimodal temporal features, and they usually fuse the features from multiple modalities with the same method and ignore the correlation between different modalities when modeling inter-modal interactions. In this paper, we first propose an Attentional Temporal Convolutional Network (ATCN) to extract unimodal temporal features for enhancing the feature representation ability, then introduce a Multi-layer Feature Fusion (MFF) model to improve the effectiveness of multimodal fusion, which fuses the different-level features by different methods according to the correlation coefficient between the features, and cross-modal multi-head attention is used to fully explore the potential relationship between the low-level features. The experimental results on SIMS and CMU-MOSI datasets show that the proposed model achieves superior performance on sentiment analysis tasks compared to state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed. [ABSTRACT FROM AUTHOR]

In the field of reversible data hiding (RDH), how to predict an image and embed a message into the image with smaller distortion are two important aspects. In this paper, we propose a novel and efficient RDH method by innovating an intelligent predictor and an adaptive embedding way. In the prediction stage, we first constructed a convolutional neural network (CNN) based predictor by reasonably dividing an image into four parts. In such a way, each part can be predicted by using the other three parts as the context for the improvement of the prediction performance. Compared with existing predictors, the proposed CNN predictor can use more neighboring pixels for the prediction by exploiting its multi-receptive fields and global optimization capacities. In the embedding stage, we also developed a prediction-error-ordering (PEO) based adaptive embedding strategy, which can better adapt image content and thus efficiently reduce the embedding distortion by elaborately and luminously applying background complexity to select and pair those smaller prediction errors for data hiding. With the proposed CNN prediction and embedding ways, the RDH method presented in this paper provides satisfactory results in improving the visual quality of data hidden images, e.g., the average PSNR value for the Kodak benchmark dataset can reach as high as 63.59 dB with an embedding capacity of 10,000 bits. Extensive experimental results have shown that the RDH method proposed in this paper is superior to those existing state-of-the-art works. [ABSTRACT FROM AUTHOR]